# Associative Learning Mechanism for Drug-Target Interaction Prediction

arXiv: https://arxiv.org/abs/2205.15364 · HTML: https://mlsanity.com/paper/2205.15364/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 71 references that are in the map.
- First posted on arXiv 24 May 2022.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [DeepDTA: Deep Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/1801.10193/) (2018)
- [MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction](https://mlsanity.com/paper/2004.11424/) (2020)
- [DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks](https://mlsanity.com/paper/1806.07537/) (2018)
- [DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences](https://mlsanity.com/paper/1811.02114/) (2018)
- [PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions](https://mlsanity.com/paper/2008.12249/) (2020)
- [SILT: Efficient transformer training for inter-lingual inference](https://mlsanity.com/paper/2103.09635/) (2021)
- [Toward Drug-Target Interaction Prediction via Ensemble Modeling and Transfer Learning](https://mlsanity.com/paper/2107.00719/) (2021)
- [Local and Global Context-Based Pairwise Models for Sentence Ordering](https://mlsanity.com/paper/2110.04291/) (2021)

## Built on by (most cited first, 4 of 4)

- [MIN: Multi-channel Interaction Network for Drug-Target Interaction with Protein Distillation](https://mlsanity.com/paper/2412.07778/) (2024)
- [Drug-Target Interaction/Affinity Prediction: Deep Learning Models and Advances Review](https://mlsanity.com/paper/2502.15346/) (2025)
- [Multi-Level Fusion Graph Neural Network for Molecule Property Prediction](https://mlsanity.com/paper/2507.03430/) (2025)
- [ImageDDI: Image-enhanced Molecular Motif Sequence Representation for Drug-Drug Interaction Prediction](https://mlsanity.com/paper/2508.08338/) (2025)

## Abstract (arXiv)

As a necessary process in drug development, finding a drug compound that can selectively bind to a specific protein is highly challenging and costly. Drug-target affinity (DTA), which represents the strength of drug-target interaction (DTI), has played an important role in the DTI prediction task over the past decade. Although deep learning has been applied to DTA-related research, existing solutions ignore fundamental correlations between molecular substructures in molecular representation learning of drug compound molecules/protein targets. Moreover, traditional methods lack the interpretability of the DTA prediction process. This results in missing feature information of intermolecular interactions, thereby affecting prediction performance. Therefore, this paper proposes a DTA prediction method with interactive learning and an autoencoder mechanism. The proposed model enhances the corresponding ability to capture the feature information of a single molecular sequence by the drug/protein molecular representation learning module and supplements the information interaction between molecular sequence pairs by the interactive information learning module. The DTA value prediction module fuses the drug-target pair interaction information to output the predicted value of DTA. Additionally, this paper theoretically proves that the proposed method maximizes evidence lower bound (ELBO) for the joint distribution of the DTA prediction model, which enhances the consistency of the probability distribution between the actual value and the predicted value. The experimental results confirm mutual transformer-drug target affinity (MT-DTA) achieves better performance than other comparative methods.